Quick Start

Create a Tree-based Tensor

You can create a tree-based tensor or a native tensor like the following example code.

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import builtins
import os
from functools import partial

import treetensor.torch as torch

print = partial(builtins.print, sep=os.linesep)

if __name__ == '__main__':
    t1 = torch.tensor([[1, 2, 3],
                       [4, 5, 6]])
    print('new native tensor:', t1)

    t2 = torch.tensor({
        'a': [1, 2, 3],
        'b': {'x': [[4, 5], [6, 7]]},
    })
    print('new tree tensor:', t2)

    t3 = torch.randn(2, 3)
    print('new random native tensor:', t3)

    t4 = torch.randn({
        'a': (2, 3),
        'b': {'x': (3, 4)},
    })
    print('new random tree tensor:', t4)

The output should be like below.

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new native tensor:
tensor([[1, 2, 3],
        [4, 5, 6]])
new tree tensor:
<Tensor 0x7f3fb2f7d250>
├── 'a' --> tensor([1, 2, 3])
└── 'b' --> <Tensor 0x7f3fb2ed6a30>
    └── 'x' --> tensor([[4, 5],
                        [6, 7]])

new random native tensor:
tensor([[-1.2680, -0.2966, -0.4790],
        [ 1.8283, -0.6857, -0.9810]])
new random tree tensor:
<Tensor 0x7f3fb2b69ee0>
├── 'a' --> tensor([[-1.4783,  2.4133, -1.8285],
│                   [-0.4211, -0.7495, -0.9413]])
└── 'b' --> <Tensor 0x7f3fb2f6b460>
    └── 'x' --> tensor([[ 0.3962, -2.1500, -0.3949,  1.1352],
                        [-0.2198, -0.6111,  0.6990, -0.2641],
                        [-0.5021, -0.3551,  0.6743,  1.1183]])